Battle of chips: Computer beats human experts at poker

Human pride took a hit 11 years ago when IBM's Big Blue computer beat world chess champion Gary Kasparov. Now it's poker players' turn to be humiliated by a machine. A computer system called Polaris outperformed some of the world's top players last weekend at a human-vs.-machine competition in Las Vegas. The score was computer 3, humans 2, with one draw.

Computer scientist Michael Bowling shows off a computer that controls Polaris, his artificial intelligence system that beat top poker players in Las Vegas last weekend.
University of Alberta, Canada / MCT

Computer scientist Michael Bowling shows off a computer that controls Polaris, his artificial intelligence system that beat top poker players in Las Vegas last weekend.
University of Alberta, Canada / MCT

WASHINGTON — Human pride took a hit 11 years ago when IBM's Big Blue computer beat world chess champion Gary Kasparov. Now it's poker players' turn to be humiliated by a machine.

A computer system called Polaris outperformed some of the world's top players last weekend at a human-vs.-machine competition in Las Vegas.

The score was computer 3, humans 2, with one draw.

If you think it should be easier for a computer to win at poker than at the highly intellectual game of chess, think again. The human element makes poker a much more complex challenge.

"Poker is a completely different game," said computer scientist Michael Bowling, the leader of a Computer Poker Research Group at the University of Alberta, Canada.

"In chess or checkers, you have perfect information. There are no secrets on the board," Bowling said. "But in poker you don't know the other person's cards. The basic computer techniques used in chess can't help you in poker."

The poker computer project may have practical applications beyond the card room. For example, Bowling said poker-like skills might be useful in bidding auctions where multiple companies are competing for government contracts or buyers are hunting deals on eBay.

"There is a lot of uncertainty there," he said. "Should you wait or bid? The same things apply in poker."

Bowling's team launched Polaris five years ago as a project in artificial intelligence. At first it did well against amateur players but couldn't beat professionals. Last year, it narrowly lost a match against two poker pros in Vancouver, British Columbia.

This year, a stronger version of Polaris — one that learns how to adapt to an opponent's strategy in midgame — triumphed over seven top-ranked humans drawn from the online poker-training site www.stoxpoker.com.

So far, the system plays a relatively simple game of two-person Texas Hold 'em. The next goal is to take on games of three or more players.

"That's very challenging," Bowling said. "There is no perfect strategy to play against multiple players."

Unlike Big Blue's IBM supercomputer, the Canadian team used a cluster of five small, off-the-shelf computers linked in a network to prepare its strategy before the game.

The system repeatedly played 8 billion games against itself to devise multiple strategies, each slightly different. Some strategies were more aggressive, others more passive.

When it came time for the match, a laptop was sufficient to manage the system. The laptop, of course, showed the perfect poker face.

During the game, Polaris analyzed its human opponent's style of play and adjusted its strategy to meet it. For example, the system plays more aggressively in order to get the human to give up and fold his cards.

"The computer pushes humans to make more decisions. More decisions mean more mistakes. Aggression raises errors," Bowling said.

Bowling conceded that it will take a few more competitions for human poker players to accept that a computer can outdo them.

"Now that we've lost, I'm itching for a rematch," said Jay Palansky, one of Polaris' opponents.